EconPapers    
Economics at your fingertips  
 

Full‐information estimation of heterogeneous agent models using macro and micro data

Laura Liu and Mikkel Plagborg‐Møller

Quantitative Economics, 2023, vol. 14, issue 1, 1-35

Abstract: We develop a generally applicable full‐information inference method for heterogeneous agent models, combining aggregate time series data and repeated cross‐sections of micro data. To handle unobserved aggregate state variables that affect cross‐sectional distributions, we compute a numerically unbiased estimate of the model‐implied likelihood function. Employing the likelihood estimate in a Markov Chain Monte Carlo algorithm, we obtain fully efficient and valid Bayesian inference. Evaluation of the micro part of the likelihood lends itself naturally to parallel computing. Numerical illustrations in models with heterogeneous households or firms demonstrate that the proposed full‐information method substantially sharpens inference relative to using only macro data, and for some parameters micro data is essential for identification.

Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.3982/QE1810

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:quante:v:14:y:2023:i:1:p:1-35

Ordering information: This journal article can be ordered from
https://www.econometricsociety.org/membership
econometrica@econometricsociety.org

Access Statistics for this article

More articles in Quantitative Economics from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery (contentdelivery@wiley.com).

 
Page updated 2024-12-29
Handle: RePEc:wly:quante:v:14:y:2023:i:1:p:1-35